Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Scarce Societal Resource Allocation and the Price of (Local) Justice
Authors: Quan Nguyen, Sanmay Das, Roman Garnett5628-5636
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide extensive experimental results using both synthetic data and in a real-world setting considering the efficacy of different homelessness interventions. |
| Researcher Affiliation | Academia | 1 Washington University in St. Louis 2 George Mason University |
| Pseudocode | Yes | Algorithm 1 Efficient leximin Input: matrix C with sorted rows Output: leximin assignment on P 1: Initialize A = (a1, a2, ..., an) as (0, 0, ..., 0). 2: for j = k, ..., 1 do 3: while uj > 0 do 4: i = arg mini,ai=0 ci,j 5: ai j 6: uj uj 1 7: end while 8: end for 9: return A |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We consider the homelessness reentry probability dataset, introduced by Kube, Das, and Fowler (2019) |
| Dataset Splits | No | The paper describes generating synthetic data and using a real-world dataset, but it does not specify explicit training, validation, or test dataset splits for model training or evaluation in the way the question implies for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | For each combination of the cost distribution and n, we ran 500 experiments with k = 5 interventions of equal capacity, and recorded the resulting Po F values. We fixed n = 30, k = 5, and uj = 6, j [5]. We draw i.i.d. samples from Beta distributions to generate random cost matrices as instances of the assignment problem. By adjusting the parameters α and β, we can simulate various distribution shapes from which costs ci,j [0, 1] are drawn. |